Datasets

• Transit ride data
  • daily: daily summary of rides
• Durham registered voter data
  • durham_voters: one row per voter

daily <- read_csv("data/daily.csv")
durham_voters <- read_csv("data/durham_voters.csv")

Your turn!

Exercise: Which of the four datasets does this visualization use? Determine which variable is mapped to which aesthetic (x-axis, y-axis, etc.) element of the dataset.

Step-by-step

ggplot(data = daily)

ggplot(data = daily, mapping = aes(x = ride_date, y = n_rides))

ggplot(data = daily, mapping = aes(x = ride_date, y = n_rides)) + 
  geom_point()

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_point()

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_smooth(method = "loess")

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_smooth(method = "loess", se = FALSE)

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_smooth(method = "loess", se = FALSE) +
  scale_color_viridis_d()

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) + 
  geom_smooth(method = "loess", se = FALSE) +
  scale_color_viridis_d() +
  theme_minimal()

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(se = FALSE, method = "loess") +
  scale_color_viridis_d() +
  theme_minimal() +
  labs(x = "Ride date", y = "Number of rides", color = "Day of week",
       title = "Daily rides", subtitle = "Durham, NC")

Size by number of riders

ggplot(data = daily, aes(x = ride_date, y = n_rides, size = n_riders)) +
  geom_point()

Set alpha value

ggplot(data = daily, aes(x = ride_date, y = n_rides, size = n_riders)) +
  geom_point(alpha = 0.5)

Your turn!

Exercise: Using information from https://ggplot2.tidyverse.org/articles/ggplot2-specs.html add color, size, alpha, and shape aesthetics to your graph. Experiment. Do different things happen when you map aesthetics to discrete and continuous variables? What happens when you use more than one aesthetic?

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(se = FALSE, method = "loess") +
  scale_color_viridis_d() +
  theme_minimal() +
  labs(x = "Ride date", y = "Number of rides", color = "Day of week",
       title = "Daily rides", subtitle = "Durham, NC")

Mappings can be at the geom level

ggplot(data = daily) +
  geom_point(mapping = aes(x = ride_date, y = n_rides))

Different mappings for different geoms

ggplot(data = daily, mapping = aes(x = ride_date, y = n_rides)) +
  geom_point() +
  geom_smooth(aes(color = day_of_week), method = "loess", se = FALSE)

Set vs. map

• To map an aesthetic to a variable, place it inside aes()

ggplot(data = daily, 
  mapping = aes(x = ride_date, 
                y = n_rides,
            color = day_of_week)) +
  geom_point() 

• To set an aesthetic to a value, place it outside aes()

ggplot(data = daily, 
  mapping = aes(x = ride_date, 
                y = n_rides)) +
  geom_point(color = "red") 

Data can be passed in

daily %>%
  ggplot(aes(x = ride_date, y = n_rides)) +
    geom_point()

Parameters can be unnamed

ggplot(daily, aes(x = ride_date, y = n_rides)) +
  geom_point()

Variable creation on the fly…

Color by weekday / weekend

ggplot(data = daily, aes(x = ride_date, y = n_rides, 
       color = day_of_week %in% c("Sat", "Sun"))) +
  geom_point()

Variable creation on the fly…

ggplot(data = daily, aes(x = ride_date, y = n_rides, 
       color = day_of_week %in% c("Sat", "Sun"))) +
  geom_point() +
  labs(color = "Weekend")

… or passed in

daily %>%
  mutate(day_type = if_else(day_of_week %in% c("Sat", "Sun"),
                            "Weekend",
                            "Weekday")) %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_type)) +
    geom_point()

Mappings that aren’t

ggplot(data = daily) +
  geom_point(aes(x = ride_date, y = n_rides, color = "blue"))

Mappings that aren’t

ggplot(data = daily) +
  geom_point(aes(x = ride_date, y = n_rides), color = "blue")

Your turn!

Exercise: What is wrong with the following?

daily %>%
  mutate(day_type = if_else(day_of_week %in% c("Sat", "Sun"),
                            "Weekend",
                            "Weekday")) %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_type)) %>%
    geom_point()

+ and %>%

What is wrong with the following?

daily %>%
  mutate(day_type = if_else(day_of_week %in% c("Sat", "Sun"),
                            "Weekend",
                            "Weekday")) %>%
  ggplot(aes(x = ride_date, y = n_rides, color = day_type)) %>%
    geom_point()

## Error: `mapping` must be created by `aes()`
## Did you use %>% instead of +?

Basic plot

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_point() 

Two layers!

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_point() +
  geom_line()

Iterate on layers

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_point() + 
  geom_smooth(span = 0.1) # try changing span

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

The power of groups

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() + 
  geom_line()

Now we’ve got it

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(span = 0.2, se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Control data by layer

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = filter(daily,
                           !(day_of_week %in% c("Sat", "Sun")) 
                           & n_rides < 200),
             size = 5, color = "gray") +
  geom_point()

Your turn!

Exercise: Work with your neighbor to sketch what the following plot will look like. No cheating! Do not run the code, just think through the code for the time being.

Step by step

low_weekdays <- daily %>%
  filter(!(day_of_week %in% c("Sat", "Sun")) & n_rides < 100)

low_weekdays

## # A tibble: 9 x 7
##   ride_date  day_of_week month n_rides n_riders n_unique_stops
##   <date>     <chr>       <chr>   <dbl>    <dbl>          <dbl>
## 1 2015-01-01 Thurs       Jan        58       37             44
## 2 2015-01-26 Mon         Jan        58       52             15
## 3 2015-01-28 Wed         Jan        79       65             11
## 4 2015-01-30 Fri         Jan        25       25             12
## 5 2015-02-03 Tues        Feb         2        2              2
## 6 2015-02-17 Tues        Feb        46       34             33
## 7 2015-02-26 Thurs       Feb        30       22             22
## 8 2015-05-25 Mon         May        99       55             66
## 9 2015-12-25 Fri         Dec         1        1              1
## # … with 1 more variable: n_unique_routes <dbl>

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point()

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  geom_point(data = low_weekdays, size = 5, color = "gray")

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = low_weekdays, size = 5, color = "gray") +
  geom_point()

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = low_weekdays, size = 5, color = "gray") +
  geom_point() +
  geom_text(data = low_weekdays, aes(y = n_rides, label = ride_date), 
            size = 2, color = "black")

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = low_weekdays, size = 5, color = "gray") +
  geom_point() +
  geom_text(data = low_weekdays, aes(y = n_rides + 15, label = ride_date), 
            size = 2, color = "black")

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = low_weekdays, size = 5, color = "gray") +
  geom_point() + 
  geom_text_repel(data = low_weekdays, 
                  aes(x = ride_date, y = n_rides, 
                      label = as.character(ride_date)), 
                  size = 3, color = "black")

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point(data = low_weekdays, size = 5, color = "gray") +
  geom_point() + 
  geom_label_repel(data = low_weekdays, 
                  aes(x = ride_date, y = n_rides, 
                      label = as.character(ride_date)), 
                  size = 2, color = "black")

Your turn!

Exercise: How would you fix the following plot?

ggplot(daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_smooth(color = "blue")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Data prep

daily <- daily %>%
  mutate(
    day = if_else(day_of_week %in% c("Sat", "Sun"), "Weekend", "Weekday"),
    temp = if_else(month %in% c("Jan", "Feb", "Mar", "Apr", "May", "Jun"),
                   "Cooler", "Warmer")
    ) %>%
  select(day, temp, everything())

daily

## # A tibble: 364 x 9
##    day   temp  ride_date  day_of_week month n_rides n_riders n_unique_stops
##    <chr> <chr> <date>     <chr>       <chr>   <dbl>    <dbl>          <dbl>
##  1 Week… Cool… 2015-01-01 Thurs       Jan        58       37             44
##  2 Week… Cool… 2015-01-02 Fri         Jan       134       83             93
##  3 Week… Cool… 2015-01-03 Sat         Jan       145       84            100
##  4 Week… Cool… 2015-01-04 Sun         Jan       101       57             63
##  5 Week… Cool… 2015-01-05 Mon         Jan       182      117            109
##  6 Week… Cool… 2015-01-06 Tues        Jan       267      138            146
##  7 Week… Cool… 2015-01-07 Wed         Jan       243      157            129
##  8 Week… Cool… 2015-01-08 Thurs       Jan       235      154            141
##  9 Week… Cool… 2015-01-09 Fri         Jan       268      173            147
## 10 Week… Cool… 2015-01-10 Sat         Jan       198      114            116
## # … with 354 more rows, and 1 more variable: n_unique_routes <dbl>

facet_wrap

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_line() +
  facet_wrap( ~ day)

facet_grid

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_line() +
  facet_grid(temp ~ day)

facet_grid

ggplot(data = daily, aes(x = ride_date, y = n_rides)) +
  geom_line() +
  facet_grid(day ~ temp)

Durham voters

durham_voters %>%
  select(race_code, gender_code, age)

## # A tibble: 204,063 x 3
##    race_code gender_code age        
##    <chr>     <chr>       <chr>      
##  1 I         M           Age Over 66
##  2 U         U           Age 18 - 25
##  3 O         F           Age 41 - 65
##  4 W         F           Age 41 - 65
##  5 W         M           Age 41 - 65
##  6 B         M           Age 26 - 40
##  7 W         F           Age 41 - 65
##  8 W         M           Age 26 - 40
##  9 B         F           Age 41 - 65
## 10 B         M           Age 41 - 65
## # … with 204,053 more rows

Data prep

durham_voters %>%
  group_by(race_code, gender_code, age) %>%
  summarize(n_voters = n(), n_rep = sum(party == "REP"))

## # A tibble: 92 x 5
## # Groups:   race_code, gender_code [21]
##    race_code gender_code age                            n_voters n_rep
##    <chr>     <chr>       <chr>                             <int> <int>
##  1 A         F           Age < 18 Or Invalid Birth Date        2     0
##  2 A         F           Age 18 - 25                         751    35
##  3 A         F           Age 26 - 40                        1086    64
##  4 A         F           Age 41 - 65                         727    75
##  5 A         F           Age Over 66                         170    36
##  6 A         M           Age 18 - 25                         635    42
##  7 A         M           Age 26 - 40                         919    64
##  8 A         M           Age 41 - 65                         572    61
##  9 A         M           Age Over 66                         175    33
## 10 A         U           Age 18 - 25                           8     1
## # … with 82 more rows

Data prep

durham_voters_summary <- durham_voters %>%
  group_by(race_code, gender_code, age) %>%
  summarize(n_all_voters = n(), n_rep_voters = sum(party == "REP")) %>%
  filter(gender_code %in% c("F", "M") & 
         race_code %in% c("W", "B", "A") &
         age != "Age < 18 Or Invalid Birth Date")

facet_grid

ggplot(durham_voters_summary, aes(x = age, y = n_all_voters)) +
  geom_bar(stat = "identity") +
  facet_grid(race_code ~ gender_code)

Free scales

ggplot(durham_voters_summary, aes(x = age, y = n_all_voters)) +
  geom_bar(stat = "identity") +
  facet_grid(race_code ~ gender_code, scales = "free_y")

Facets + layers

Facets + layers

Using new tidyr function: pivot_longer()

durham_voters_summary %>% 
  tidyr::pivot_longer(cols = starts_with("n_"), 
                      names_to = "voter_type", values_to = "n", 
                      names_prefix = "n_") %>%
  mutate(age_cat = as.numeric(as.factor(age))) %>%
  ggplot(aes(x = age, y = n, color = voter_type)) + 
    geom_point() + 
    geom_line(aes(x = age_cat)) +
    facet_grid(race_code ~ gender_code, scales = "free_y") +
    expand_limits(y = 0)

Scale transformation

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_reverse()

Scale transformation

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_sqrt()

Scale details

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  scale_y_continuous(breaks = c(0, 200, 500))

Overall themes

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_bw()

Overall themes

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme_dark()

Customizing theme elements

ggplot(data = daily, aes(x = ride_date, y = n_rides, color = day_of_week)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 90))

Your turn!

Exercise: Fix the axis labels in the following plot so they are at a 45 degree angle.

ggplot(durham_voters_summary, aes(x = age, y = n_all_voters)) +
  geom_bar(stat = "identity") +
  facet_grid(race_code ~ gender_code, scales = "free_y")